Image Denoising Using Different Filters

Image processing is basically the use of computer algorithms to perform image processing on digital images. Image denoising adds the manipulation of the image to produce a high quality image. The main criteria of Image denoising are to restore the detail of original image as much as possible. Image processing provides much range of algorithms to be applied to the input data and can remove problems such as the increase of noise and signal distortion during processing of images. Different types of noise models including additive and multiplication types are used. In this work four types of noise (Amplifier noise, Salt & Pepper noise, Speckle noise and Poisson noise) is used and image de-noising performed for different noise by Inverse filter, Wiener filter and Lucy-Richardson method. Selection of the denoising algorithm is based on the using noise and filter in image processing. Hence, it is very important to know about the noise present in the image and select the appropriate denoising algorithm. The filtering approach has defined the best results when the image is corrupted with salt and pepper noise. In this paper, we introduce some important type of noise and a comparative analysis of noise removal techniques is applied. The experimental results are discussed and analyzed to determine the overall advantages and disadvantages of each category.